Full Fine‑Tuning vs LoRA vs RAG: How to Choose for LLMs

Description

This video compares three ways to adapt large language models: full fine‑tuning for deep behavior changes, LoRA adapters for fast and cheap specialization, and RAG for grounding answers in up‑to‑date knowledge without retraining. Walk through how each works, the trade‑offs in cost, latency, data needs, and maintenance, plus real‑world stacks that combine RAG for freshness with small LoRA adapters for style—reserving full fine‑tunes for fundamental behavior shifts. What you’ll learn: How full fine‑tuning, LoRA, and RAG differ in mechanics and outcomes When to choose each: behavior change vs efficiency vs knowledge freshness Practical trade‑offs: compute, data curation, latency, provenance, and safety Production patterns: hybrid RAG + LoRA, evaluation, and monitoring tips Chapters: 0:00 Intro 0:25 Full fine‑tuning explained 1:05 LoRA adapters and when they shine 1:45 RAG pipeline and grounding benefits 2:20 Choosing the right approach 2:45 Hybrid patterns and takeaways